The linearity and efficiency of radio frequency (RF) power amplifiers (PAs) have been a critical design issue for non-constant envelope digital modulation schemes which have high peak-to-average-power ratios (PARs) as the importance of spectral efficiency in wireless communication systems increases. RF Pas have nonlinearities that generate amplitude modulation—amplitude modulation (AM-AM) and amplitude modulation—phase modulation (AM-PM) distortion at the output of the PA. These effects create spectral regrowth in the adjacent channels and in-band distortion which degrades the error vector magnitude (EVM).
The relationship between linearity and efficiency is a tradeoff since power efficiency is very low when the amplifier operates in its linear region and increases as the amplifier is driven into its compression region. In order to enhance linearity and efficiency at the same time, linearization techniques are typically applied to the RF PAs. Various linearization techniques have been proposed such as feedback, feedforward and predistortion.
One technique is baseband digital predistortion (PD) which typically uses a digital signal processor. Digital predistortion can achieve improved linearity and improved power efficiency with reduced system complexity when compared to the widely used conventional feedforward linearization technique. A software implementation provides the digital predistorter with re-configurability suitable for multi-standards environments. In addition, a PA using an efficiency enhancement technique such as a Doherty power amplifier (DPA) is able to achieve higher efficiencies than traditional PA designs at the expense of linearity. Therefore, combining digital predistortion with a PA using an efficiency enhancement technique has the potential to improve system linearity and overall efficiency.
However, most digital PDs presuppose that PAs have no memory or a weak memory. This is impractical in wideband applications where memory effects cause the output signal to be a function of current as well as past input signals. The sources of memory effects in PAs include self-heating of the active device (also referred to as long time constant or thermal memory effects) and frequency dependencies of the active device, related to the matching network or bias circuits (also referred to as short time constant or electrical memory effects). As signal bandwidth increases, memory effects of PAs become significant and limit the performance of memoryless digital PDs.
Various approaches have been suggested for overcoming memory effects in digital PDs. For the short-term memory effects, a Volterra filter structure was applied to compensate memory effects using an indirect learning algorithm, but the number of optimization coefficients is very large as the order increases. This complexity makes the Volterra filter based PD extremely difficult to implement in real hardware. A memory polynomial structure, which is a simplified version of the Volterra filter, has been proposed in order to reduce the number of coefficients, but even this simplified version still requires a large computational load. In addition, such a memory polynomial based PD suffers from a numerical instability when higher order polynomial terms are included because a matrix inversion is required for estimating the polynomial coefficients. An alternative, yet equally complex structure based on orthogonal polynomials has been utilized to alleviate the numerical instability associated with the traditional polynomials. To further reduce the complexity at the expense of the performance, the Hammerstein predistorter, which is a finite impulse response (FIR) filter or a linear time invariant (LTI) system followed by a memoryless polynomial PD, has been proposed. The Hammerstein predistorter assumed that the PA models used follow a Wiener model structure which is a memoryless nonlinearity followed by a finite impulse response (FIR) filter or a linear time invariant (LTI) system.
This implementation means that the Hammerstein structure can only compensate for memory effects coming from the RF frequency response. Therefore, if the RF frequency response is quite flat, the Hammerstein PD cannot correct for any other types of memory effects, such as bias-induced and thermal memory effects.
Most recently, a static lookup table (LUT) digital baseband PD cascaded with a sub-band filtering block has been used in order not to compensate for electrical memory effects, but to combat gain and phase variation due to temperature changes of the PA after an initial setting for the fixed LUT PD.
Hence, there has been a long-felt need for a baseband predistortion linearization method able to compensate for not only RF frequency response memory effects but also bias-induced or thermal memory effects in multi-channel wideband wireless transmitters.